1. Field of the Invention
The subject matter described herein relates generally to imaging and, more particularly, to imaging occlusions in vascular tissue.
2. Related Art
Diagnostic systems provide, e.g., in the field of medicine, medical personnel with information necessary to better diagnose a medical condition. For example where a patient is complaining of severe chest pain, an occlusion or a blockage such as a pulmonary embolism (PE) is one of number of possible causes that must be ruled out. In the past, the occurrence of PE was diagnosed through a visual assessment by a radiologist. This is both tedious and error-prone.
Accordingly, it has been proposed to use computer-aided detection (CAD) for pulmonary embolisms which, to date, has not enjoyed significant success. Most current methods today for CAD hinge primarily on the lowered local intensity of embolized regions relative to the immediate vessel vicinity.
One disadvantage to such an approach is that it is prone to concomitantly detecting vessel bifurcations, lymph nodes, pulmonary veins with low contrast, and other normal anatomy, as false positive detections. The detection task is further hindered because of considerable variation in absolute contrast-levels, and the distribution of contrast, between different cases. In addition, contrast-pooling effects and other imaging artifacts such as motion, that alter intensity, further complicate the analysis.
One attempt to overcome false positive detection is described in the publication Y. Masutani, H. MacMahon, K. Doi, “Computerized Detection of Pulmonary Embolism in Spiral CT Angiography Based on Volumetric Image Analysis”, IEEE Transactions On Medical Imaging 2002, 21(12), 1517). This publication describes detecting PE using local intensity contrast to identify groups of voxels within vasculature that are less opacified relative to their neighbors along with using curvilinearity properties of voxels to detect PE. A classifier developed using a training database of cases is employed to overcome false positive detection. Disadvantages to this approach include that it requires significant time to develop the training database and that it is very fragile if the acquisition parameters change between the training data set and clinical practice.
Liang et al (J. Liang, M. Wolf and M. Salganicoff, “A Fast Toboggan-based Method for Automatic Detection and Segmentation of Pulmonary Embolism in CT Angiography”, MICCAI 2005 Short Papers) describe detecting emboli in the range of −50 HU to 100 HU using a Toboggan algorithm that clusters voxels locally, by mapping every voxel to the lowest intensity voxel in proximity to it. One disadvantage to this approach is that since contrast CT cases are prone to variations in intensity, assumptions about the intensity ranges for emboli are likely to be inadequate in cases of exceptionally severe emboli, or in cases where partial volume effects produce artificially elevated intensity levels.
Zhou et al (C. Zhou et al., “Preliminary Investigation of Computer-aided Detection of Pulmonary Embolism in Three dimensional Computed Tomography Pulmonary Angiography Images”, Acad Radiol 2005; 12:782) employ a three-tiered Expectation Maximization algorithm to develop a semi-automated method for segmenting PE.
Also, attempts have been made to develop automated methods for PE visualization. For example, E. Pichon, C. L. Novak, A. P. Kiraly, “System and method for visualization of PE from high resolution CT images”, US Patent Application Pub. No. US2005/0240094 A1 and E. Pichon, C. L. Novak, A. P. Kiraly, D. Naidich, “A novel method for pulmonary emboli visualization from high-resolution CT images”, Medical Imaging 2004: Proc. SPIE Vol. 5367 each describe a maximum-descent technique to compute the statistics of vessel voxels radially to a centerline. In this way, a suitable statistic (minimum/average) of this set is assigned to all the voxels along the path to the centerline, bringing interiorly located PEs to the vessel surface. In another example, A. P. Kiraly, C. L. Novak, “System and Method for Tree Projection for Detection of Pulmonary Embolism”, U.S. Patent Application Publication No. US2006/0025674 A1, describes a variant of Pichon et al's method which uses a cart-wheel projection in lieu of a maximum-descent. In another example, A. P. Kiraly, C. L. Novak, “System and Method for Tree-Model Visualization for Pulmonary Embolism Detection”, U.S. Patent Application Publication No. US2006/0023925 A1 describes a minimum-intensity projection method that was used and the resultant vessel surface was unrolled to create a two dimensional representation of the vessel, which was used to highlight PE locations in a two dimensional delineation of the complete vessel tree. In a further example, A. P. Kiraly, E. Pichon, D. Naidich, C. L. Novak, “Analysis of arterial sub-trees affected by Pulmonary Emboli”, Medical Imaging 2004: Image Processing, Proc. SPIE Vol. 5370 describes a method whereby, given a location of an embolism, the affected lung area may be identified by extracting an arterial tree distal to the PE location.
However, to date, no suitable device or method of detecting occlusions is available which overcomes the problems and disadvantages described above.